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Found 12 papers, 8 papers with code
A Compact and Semantic Latent Space for Disentangled and Controllable Image Editing
Recent advances in the field of generative models and in particular generative adversarial networks (GANs) have lead to substantial progress for controlled image editing, especially compared with the pre-deep learning era.
Infusion: Internal Diffusion for Video Inpainting
We show that in the case of video inpainting, thanks to the highly auto-similar nature of videos, the training of a diffusion model can be restricted to the video to inpaint and still produce very satisfying results.
Patch-Based Stochastic Attention for Image Editing
We refer to our proposed layer as a "Patch-based Stochastic Attention Layer" (PSAL).
Feature-Style Encoder for Style-Based GAN Inversion
Additionally, we demonstrate that the proposed encoder is especially well-suited for inversion and editing on videos.
A Latent Transformer for Disentangled Face Editing in Images and Videos
Previous works that attempt to tackle this problem may suffer from the entanglement of facial attributes and the loss of the person's identity.
Multi-View Radar Semantic Segmentation
Understanding the scene around the ego-vehicle is key to assisted and autonomous driving.
PCAAE: Principal Component Analysis Autoencoder for organising the latent space of generative networks
We hope that this approach will contribute to better understanding of the intrinsic latent spaces of powerful deep generative models.
Processsing Simple Geometric Attributes with Autoencoders
This results in a wide range of practical problems, such as difficulties in training, the tendency to sample images with little or no variability, and generalisation problems.
Taking Apart Autoencoders: How do They Encode Geometric Shapes ?
We study the precise mechanisms which allow autoencoders to encode and decode a simple geometric shape, the disk.
Video Inpainting of Complex Scenes
Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background.
